Primates are particularly adept at directing their hand to a visual target, even when the target is moving in an uncertain way. Visually guided movement is fundamental to many behaviors, but the nature of cortical coding of this behavior is not understood. The process of using vision for manual tracking engages a collection of cortical areas. Primary motor cortex (Ml) is one important area near the final stages of hand motion control. Single neuron recordings in behaving animals have demonstrated that information about movement direction, velocity, speed, position and acceleration, as well as force can be obtained from the firing rate of single Ml neurons (e.g., see Ashe and Georgopoulos, 1994), suggesting that each of these movement features may be coded in Ml. However, understanding what and how information about hand motion is coded in Ml has been difficult to determine. Firing rates of individual neurons may combine information about multiple kinematic features (Ashe, J. & Georgopoulos, A. Movement parameters and neural activity in motor cortex and area 5. Cerebral Cortex 4, 590-600. 1994) that may be specified separately in time (Fu, Q. G., Flament, D., Coltz, J. D. & Ebner, T. J. Temporal encoding of movement kinematics in the discharge of primate primary motor and premotor neurons. Journal of Neurophysiology 73, 836-854 1995). Attempts at decoding the information carried in Ml neurons has shown that averaging firing of groups of neurons provides a reasonable estimate of some of these parameters, particularly the direction of intended hand movement, further supporting the view that these features are processed in Ml.
One major difficulty in understanding coding of hand motion in MI is relating behavior to neural activity. Most prior studies treat motor variables not as time-varying signals, but as static quantities. The properties of neurons are often summarized from the average direction of hand motion or the static, over learned location of movement targets, rather than a time varying code for hand motion that is being continuously guided directly by vision. Static tasks present a number of difficulties in addressing the combined spatial and temporal aspects encoded by MI neurons. First, the amount of space sampled for each variable is limited. Studies of direction coding are typically limited to a small subset of possible target directions (eight, in the widely used ‘center-out task’). A second problem with such tasks is that there is limited control over variables—how a hand moves between targets is a function of the animal's strategy, not the experiment's design. Consequently various statistical dependencies can appear in hand motion, including position and velocity and movement speed and initiation. Further, firing rate (which are usually actively sought for their high modulation rate) is often correlated with these multiple variables. Third, highly parametric models of firing are assumed. Typically, firing rates are reduced to a cosine function, thereby removing more complex structure that may be present in a neuron's firing pattern (Sanger T D. Probability density estimation for the interpretation of neural population codes. J Neurophysiol. October ; 764 :2790-3. 1996). Fourth, these tasks introduce non-stationarities in which neural and behavioral signals co-vary in association with various trial based epochs, making it difficult or incorrect to evaluate motion quantitatively. For example, neurons may have fundamentally different firing regimes during hold and movement periods so that it is difficult to dissociate changes in the apparent relationship between neurons and motor variables from epoch-related aspects of neural encoding (Fu et al., 1995; Maynard, E. M., Hatsopoulos, N. G., Ojakangas, C. L., Acuna, B. D., Sanes, J. N., Normann, R. A. & Donoghue, J. P. Neuronal interactions improve cortical population coding of movement direction. Journal of Neuroscience 19, 8083-809. 1999; Georgopoulos, A. P., Lurito, J. T., Petrides, M., Schwartz, A. B. & Massey, J. T. Mental rotation of the neuronal populaiton vector. Science 243, 234-236 1989). Fifth, it is difficult to compare the detailed features of neural encoding because neurons are recorded serially under behavior, neural or state conditions which may vary for each neuron.